Acinetobacter baumannii is a dangerous nosocomial pathogen associated with antimicrobial resistance in clinical infections. Although traditional antimicrobial resistance characterization relies on susceptibility testing in research or clinical laboratories, databases of resistance-associated genes are now available to characterize resistance profiles, based on the analysis of whole-genome sequence (WGS) data. In this study, we propose to sequence 100 isolates that currently have associated antimicrobial resistance data in order to better understand mechanisms associated with antimicrobial resistance. WGS data from paired resistant/susceptible isolates, based on a shared phylogenetic history, will be compared to identify both single nucleotide polymorphisms (SNPs) and coding regions associated with the resistance phenotype. A genome-wide association study (GWAS) will be employed in this study to identify SNPs and genes associated with the antimicrobial resistance phenotype. In addition to sequencing these isolates, we also propose to comprehensively characterize the antimicrobial resistance profile to associate specific antimicrobials with specific genetic features. These results are expected to generate more accurate and descriptive genotype/phenotype associations than currently exist. In addition to genomics approaches, transcriptomic methods are proposed to better understand the contribution of gene expression to the antimicrobial resistance phenotype. We will sequence extracted RNA from paired resistant/susceptible isolates grown in media with and without sub-inhibitory concentrations of antimicrobials. Differential gene expression will be identified between paired isolates to find correlations between antimicrobial resistance and gene expression. Additional antimicrobials will be tested with quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) to identify if observed transcriptional differences are associated with multiple classes of antimicrobials. In general, this proposed work is anticipated to identify novel mechanisms associated with antimicrobial resistance as well as increase the target specificity of previously associated mechanisms. These goals will not only help in the design of more informative diagnostics, but will also aid in accurate detection of antimicrobial resistance, which will also help in the more timely and focused treatment of resistant clinical infections.